Prostate Tissue Texture Feature Extraction for Suspicious Regions Identification on TRUS Images

Abstract

In this work, two different approaches are proposed for region of interest (ROI) segmentation using transrectal ultrasound (TRUS) images. The two methods aim to extract informative features that are able to characterize suspicious regions in the TRUS images. Both proposed methods are based on multi-resolution analysis that is characterized by its high localization in both the frequency and the spatial domains. Being highly localized in both domains, the proposed methods are expected to accurately identify the suspicious ROIs. On one hand, the first method depends on a Gabor filter that captures the high frequency changes in the image regions. On the other hand, the second method depends on classifying the wavelet coefficients of the image. It is shown in this paper that both methods reveal details in the ROIs which correlate with their pathological representations. It was found that there is a good match between the regions identified using the two methods, a result that supports the ability of each of the proposed methods to mimic the radiologist's decision in identifying suspicious regions. Studying two ROI segmentation methods is important since the only available dataset is the radiologist's suspicious regions, and there is a need to support the results obtained by either one of the proposed methods. This work is mainly a preliminary proof of concept study that will ultimately be expanded to a larger scale study whose aim will be introducing an assisting tool to help the radiologist identify the suspicious regions.

Adaptable texture‐based segmentation by variance and intensity for automatic detection of semitranslucent and pink blush areas in basal cell carcinoma

“…This network is so well described by the second-order texture feature correlation that no other second-order texture feature provides additional information. Recent research has explored texture segmentation using filter methods such as Gabor filters (19,20), fractal methods combined with local intensity (21), combinations of LoG-fractal methods (22), and combined first and second-order GLCM statistics (23). Yet GLCM, structural, and filter methods are all more computationally intensive than first-order statistics.…”

“…Recent research has explored texture segmentation using filter methods such as Gabor filters , fractal methods combined with local intensity , combinations of LoG‐fractal methods , and combined first and second‐order GLCM statistics . Yet GLCM, structural, and filter methods are all more computationally intensive than first‐order statistics.…”

A survey of prostate modeling for image analysis

“…Rafiee et al [50] Appearance Statistical Mohamed et al [68] Appearance Statistical Song et al [75] Shape and appearance Graph-theoretic framework Liu et al [36] Shape Deformable ellipse Segmentation and clustering Kachouie and Fieguth [63] and Kachouie et al [35] Appearance A medical texture local binary pattern operator Richard et al [60] Appearance Law's mask Classification…”

“…For this purpose, different imaging modalities and sequences are used to enhance both the specificity and sensitivity of the detection [68,135]. For this application, registration is required to place all the images in the same spatial reference.…”

“…Transformation-based approaches, such as the Gabor filter response [66][67][68][69] and wavelet [70][71][72] methods, represent an image in a space in which the coordinate system can be interpreted in a manner that is related to the characteristics of the texture, such as the frequency [73].…”